Probing dynamic myocardial microstructure with cardiac magnetic resonance diffusion tensor imaging

This article is an invited editorial comment on the paper entitled “In vivo cardiovascular magnetic resonance diffusion tensor imaging shows evidence of abnormal myocardial laminar orientations and mobility in hypertrophic cardiomyopathy” by Ferreira et al., and published as Journal of Cardiovascular Magnetic Resonance 2014; 16:87

Meshless deformable models for LV motion analysis

We propose a novel meshless deformable model for in vivo cardiac left ventricle (LV) 3D motion estimation. As a relatively new technology, tagged MRI (tMRI) provides a direct and noninvasive way to reveal local deformation of the myocardium, which creates a large amount of heart motion data which requiring quantitative analysis. In our study, we sample the heart motion sparsely at intersections of three sets of orthogonal tagging planes and then use a new meshless deformable model to recover the dense 3D motion of the myocardium temporally during the cardiac cycle. We compute external forces at tag intersections based on tracked local motion and redistribute the force to meshless particles throughout the myocardium. Internal constraint forces at particles are derived from local strain energy using a Moving Least Squares (MLS) method. The dense 3D motion field is then computed and updated using the Lagrange equation. The new model avoids the singularity problem of mesh-based models and is capable of tracking large deformation with high efficiency and accuracy. In particular, the model performs well even when the control points (tag intersections) are relatively sparse. We tested the performance of the meshless model on a numerical phantom, as well as in vivo heart data of healthy subjects and patients. The experimental results show that the meshless deformable model can fully recover the myocardium motion in 3D. 1

Dynamic subcellular localization of a respiratory complex controls bacterial respiration

International audienceRespiration, an essential process for most organisms, has to optimally respond to changes in the metabolic demand or the environmental conditions. The branched character of their respiratory chains allows bacteria to do so by providing a great metabolic and regulatory flexibility. Here, we show that the native localization of the nitrate reductase, a major respiratory complex under anaerobiosis in Escherichia coli, is submitted to tight spatiotemporal regulation in response to metabolic conditions via a mechanism using the transmembrane proton gradient as a cue for polar localization. These dynamics are critical for controlling the activity of nitrate reductase, as the formation of polar assemblies potentiates the electron flux through the complex. Thus, dynamic subcellular localization emerges as a critical factor in the control of respiration in bacteria

Reducing the Risk and Prevalence of Cannabis Use Disorder in Queer High Schoolers

Over the past few decades, the nature of cannabis in American society has changed dramatically. Cannabis is steadily increasing in potency and making a mainstream entrance into our society as it is gradually decriminalized and legalized around the country. With these changes comes a need to investigate the effects of the increasing reach of cannabis on adolescents. Particularly, high schoolers are at an age when they are slowly gaining autonomy and seeking to make adult decisions. Specifically, queer youth face injustice and inequity when it comes to housing, schooling, parenting, and medical care; all of these factors combine into increased risk for cannabis use disorder (CUD) in queer youth. This paper will outline the ideal combination of protective factors and reduction of risk factors created through individual-, school-, and policy-level interventions that would hopefully reduce the risk and prevalence of CUD in queer high schoolers

Final 5-year clinical and echocardiographic results for treatment of severe aortic stenosis with a self-expanding bioprosthesis from the ADVANCE Study.

Aims: The ADVANCE study was designed to evaluate the safety and effectiveness of transcatheter aortic valve implantation (TAVI) with a self-expanding bioprosthesis in real-world patients with symptomatic, severe aortic stenosis at high surgical risk for valve replacement. Methods and results: Study participants were enrolled from 44 experienced centres in 12 countries. Patient eligibility, treatment approach, and choice of anaesthesia were determined by the local Heart Team. The study was 100% monitored, and adverse events were adjudicated by an independent clinical events committee using Valve Academic Research Consortium (VARC-1) criteria. There were 1015 patients enrolled with 996 attempted TAVI procedures. Mean age was 81 years, and mean logistic EuroSCORE was 19.3 ± 12.3%. Five-year follow-up was available on 465 (46.7%) patients. At 5 years, the rate of all-cause mortality was 50.7% (95% confidence interval: 46.7%, 54.5%), and the rate of major stroke was 5.4%. Haemodynamic measures remained consistent for paired patients with a mean aortic valve gradient of 8.8 ± 4.4 mmHg (n = 198) and an effective orifice area of 1.7 ± 0.4 cm2 (n = 123). Aortic regurgitation (AR) decreased over time and among paired patients dropped from 12.8% to 8.0% moderate AR at 5 years (n = 125). Of the 860 patients with echocardiographic data or a reintervention after 30 days, there were 22 (2.6%) patients meeting the VARC-2 criteria for valve dysfunction and 10 (1.2%) patients with a reintervention >30 days. Conclusion: Five-year results in real-world, elderly, high-risk patients undergoing TAVI with a self-expanding bioprosthesis provided evidence for continued valve durability with low rates of reinterventions and haemodynamic valve dysfunction. Trial registration: ClinicalTrials.gov, NCT01074658

Three-Dimensional Motion Reconstruction and Analysis of the Right Ventricle Using Tagged MRI

Right ventricular (RV) dysfunction can serve as an indicator of heart and lung disease and can adversely affect the left ventricle (LV). However, normal RV function must be characterized before abnormal states can be detected. We can describe a method for reconstructing the 3D motion of the RV images by fitting of a deformable model to extracted tag and contour data from multiview tagged magnetic resonance images(MRI). The deformable model is a biventricular finite element mesh built directly from the contours. Our approach accommodates the geometrically complex RV by using the entire lengths of the tags, localized degrees of freedom (DOFs), and finite elements for geometric modeling. We convert the results of the reconstruction into potentially useful motion variables, such as strains and displacements. The fitting technique is applied to synthetic data, two normal hearts, and a heart with right ventricular hypertrophy (RVH). The results in this paper are limited to the RV free wall and septum. We find noticeable differences between the motion variables calculated for the normal volunteers and the RVH patient

Model-based Analysis of Cardiac Motion from Tagged MRI Data

We develop a new method for analyzing the motion of the left ventricle (LV) of a heart from tagged MRI data. Our technique is based on the development of a new class of physics-based deformable models whose parameters are functions allowing the definition of new parameterized primitives and parameterized deformations. These parameter functions improve the accuracy of shape description through the use of a few intuitive parameters such as functional twisting. Furthermore, these parameters require no complex post-processing in order to be used by a physician. Using a physics-based approach, we convert these geometric models into deformable models that deform due to forces exerted from the datapoints and conform to the given dataset. We present experiments involving the extraction of shape and motion of the LV from MRI-SPAMM data based on a few parameter functions. Furthermore, by plotting the variations over time of the extracted model parameters from normal and abnormal heart data we are able to characterize quantitatively their differences

Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction

The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information. Currently, the unrolled model, an approach combining iterative MRI reconstruction steps with learnable neural network layers, stands as the best-performing method for MRI reconstruction. However, there are two main limitations to overcome: firstly, the unrolled model structure and GPU memory constraints restrict the capacity of each denoising block in the network, impeding the effective extraction of detailed features for reconstruction; secondly, the existing model lacks the flexibility to adapt to variations in the input, such as different contrasts, resolutions or views, necessitating the training of separate models for each input type, which is inefficient and may lead to insufficient reconstruction. In this paper, we propose a two-stage MRI reconstruction pipeline to address these limitations. The first stage involves filling the missing k-space data, which we approach as a physics-based reconstruction problem. We first propose a simple yet efficient baseline model, which utilizes adjacent frames/contrasts and channel attention to capture the inherent inter-frame/-contrast correlation. Then, we extend the baseline model to a prompt-based learning approach, PromptMR, for all-in-one MRI reconstruction from different views, contrasts, adjacent types, and acceleration factors. The second stage is to refine the reconstruction from the first stage, which we treat as a general video restoration problem to further fuse features from neighboring frames/contrasts in the image domain. Extensive experiments show that our proposed method significantly outperforms previous state-of-the-art accelerated MRI reconstruction methods.Comment: STACOM 2023; Code is available at https://github.com/hellopipu/PromptM

Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging

We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic resonance (CMR) imaging data. We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions and are capable of deforming globally and locally. While global geometric parameter functions and deformations capture gross shape features from visual data, local deformations, parameterized as neural diffeomorphic point flows, can be learned to recover the detailed heart shape.Different from iterative optimization methods used in conventional deformable model formulations, NDMs can be trained to learn such geometric parameter functions, global and local deformations from a shape distribution manifold. Our NDM can learn to densify a sparse cardiac point cloud with arbitrary scales and generate high-quality triangular meshes automatically. It also enables the implicit learning of dense correspondences among different heart shape instances for accurate cardiac shape registration. Furthermore, the parameters of NDM are intuitive, and can be used by a physician without sophisticated post-processing. Experimental results on a large CMR dataset demonstrate the improved performance of NDM over conventional methods.Comment: Accepted by ICCV 202